Aether AI, founded by UCSD professor Biwei Huang, closed a $20 million seed round on June 18, 2026 to build causal world models that understand cause-and-effect relationships rather than statistical ...

We dedicate this short chapter to the DoWhy framework, which provides the structure for the causal methods explored later in Chapter 5–8 in this book. DoWhy includes a wide range of estimators — such ...

What if every decision you made left behind an echo – an imprint of your past actions, repeating endlessly? In Causal Loop, players don’t just solve puzzles—they navigate a fractured reality that is ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

Over a decade ago, when I was first starting to pretend I could write about quantum mechanics, I covered a truly bizarre experiment. One half of a pair of entangled photons was sent through a device ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...

Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...

Animals survive in changing and unpredictable environments by not merely responding to new circumstances, but also, like humans, by forming inferences about their surroundings—for instance, squirrels ...

Inferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that making causal inferences about biological processes (e.g. illness) depends on the ...